AI-Enhanced Real-Time Monitoring of Marine Pollution: Part 2—A Spectral Analysis Approach

Oil spills and marine litter pose significant threats to marine ecosystems, necessitating innovative real-time monitoring solutions. This research presents a novel AI-driven multisensor system that integrates RGB, thermal infrared, and hyperspectral radiometers to detect and classify pollutants in dynamic offshore environments. The system features a dual-unit design: an overview unit for wide-area detection and a directional unit equipped with an autonomous pan-tilt mechanism for focused high-resolution analysis. By leveraging multi-hyperspectral data fusion, this system overcomes challenges such as variable lighting, water surface reflections, and environmental interferences, significantly enhancing pollutant classification accuracy. The YOLOv5 deep learning model was validated using extensive synthetic and real-world marine datasets, achieving an F1-score of 0.89 and an mAP of 0.90. These results demonstrate the robustness and scalability of the proposed system, enabling real-time pollution monitoring, improving marine conservation strategies, and supporting regulatory enforcement for environmental sustainability.